Who Made This? Algorithms and Authorship Credit.

Area of Science:

• Cognitive Science
• Human-Computer Interaction
• Sociology of Technology

Background:

• Creators and producers traditionally receive assistance from human collaborators.
• Algorithms are increasingly capable of providing similar forms of assistance in creative and productive tasks.
• The impact of algorithmic assistance on the attribution of credit to human producers is an emerging area of inquiry.

Purpose of the Study:

• To investigate whether algorithmic assistance influences the credit assigned to producers compared to human assistance.
• To explore the underlying mechanisms, such as perceived oversight, that might explain differences in credit attribution.
• To examine the downstream effects of credit attribution on entrepreneurial support for producers.

Main Methods:

• Conducted four empirical studies across diverse domains including art, construction, sports analytics, and entrepreneurship.
• Employed experimental designs to compare credit attribution for producers assisted by algorithms versus humans.
• Utilized surveys and behavioral measures to assess perceived oversight and entrepreneurial support.

Main Results:

• Producers receive significantly more credit when assisted by algorithms compared to human collaborators.
• Perceived need for greater producer oversight of algorithmic assistance mediates the increased credit attribution.
• Enhanced credit for algorithmically-assisted producers translates into greater support for their entrepreneurial ventures.

Conclusions:

• Algorithmic assistance alters traditional norms of credit and authorship.
• The perception of increased control and oversight in human-algorithm collaboration drives higher credit assignment.
• These shifts have potential economic and social implications as algorithmic integration in production grows.